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Record W4390580164 · doi:10.1164/rccm.202312-2213so

Meaningful Endpoints for Idiopathic Pulmonary Fibrosis (IPF) Clinical Trials: Emphasis on ‘Feels, Functions, Survives’. Report of a Collaborative Discussion in a Symposium with Direct Engagement from Representatives of Patients, Investigators, the National Institutes of Health, a Patient Advocacy Organization, and a Regulatory Agency

2024· article· en· W4390580164 on OpenAlexafffund
Ganesh Raghu, Marya Ghazipura, Thomas R. Fleming, Kerri I. Aronson, Jürgen Behr, Kevin M. Brown, Kevin R. Flaherty, Ella A. Kazerooni, Toby M. Maher, Luca Richeldi, Joseph A. Lasky, Jeffrey J. Swigris, Robert Busch, Lili Garrard, Dong-Hyun Ahn, Ji Li, Khalid Puthawala, Gabriela Rodal, Sally Seymour, Nargues Weir, Sonye K. Danoff, Neil Ettinger, Jonathan Goldin, Marilyn K. Glassberg, Letícia Kawano-Dourado, Nasreen Khalil, Lisa Lancaster, David A. Lynch, Yolanda Mageto, Imre Noth, Jessica Shore, Marlies Wijsenbeek, Robert S. Brown, Daniel Grogan, Dorothy Ivey, Patrycja Golińska, Banu A. Karimi-Shah, Fernando J. Martínez

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsUniversity of British Columbia
FundersNational Heart, Lung, and Blood InstituteWeill Cornell Medical CollegeNational Institutes of HealthImperial College LondonUniversité de LilleYork UniversityMcMaster University
KeywordsMedicineClinical trialClinical endpointIdiopathic pulmonary fibrosisIntensive care medicineSurrogate endpointPirfenidoneInternal medicineLung

Abstract

fetched live from OpenAlex

Abstract Background Idiopathic pulmonary fibrosis (IPF) carries significant mortality and unpredictable progression, with limited therapeutic options. Designing trials with patient-meaningful endpoints, enhancing the reliability and interpretability of results, and streamlining the regulatory approval process are of critical importance to advancing clinical care in IPF. Methods A landmark in-person symposium in June 2023 assembled 43 participants from the US and internationally, including patients with IPF, investigators, and regulatory representatives, to discuss the immediate future of IPF clinical trial endpoints. Patient advocates were central to discussions, which evaluated endpoints according to regulatory standards and the FDA’s ‘feels, functions, survives’ criteria. Results Three themes emerged: 1) consensus on endpoints mirroring the lived experiences of patients with IPF; 2) consideration of replacing forced vital capacity (FVC) as the primary endpoint, potentially by composite endpoints that include ‘feels, functions, survives’ measures or FVC as components; 3) support for simplified, user-friendly patient-reported outcomes (PROs) as either components of primary composite endpoints or key secondary endpoints, supplemented by functional tests as secondary endpoints and novel biomarkers as supportive measures (FDA Guidance for Industry (Multiple Endpoints in Clinical Trials) available at: https://www.fda.gov/media/162416/download). Conclusions This report, detailing the proceedings of this pivotal symposium, suggests a potential turning point in designing future IPF clinical trials more attuned to outcomes meaningful to patients, and documents the collective agreement across multidisciplinary stakeholders on the importance of anchoring IPF trial endpoints on real patient experiences—namely, how they feel, function, and survive. There is considerable optimism that clinical care in IPF will progress through trials focused on patient-centric insights, ultimately guiding transformative treatment strategies to enhance patients’ quality of life and survival.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.354
metaresearch head score (Gemma)0.299
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.646
Threshold uncertainty score0.797

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3540.299
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.002
Science and technology studies0.0160.019
Scholarly communication0.0210.016
Open science0.0050.033
Research integrity0.0210.058
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.379
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations76
Published2024
Admission routes2
Has abstractyes

Explore more

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